Modelling the human hand

The human hand is a complex organ capable of both gross grasp and fine motor skills. Despite many successful high-level skeletal control techniques, animating realistic hand motion remains tedious and challenging. It is widely believed that it can be modelled with 27 degrees of freedom (DOFs), based on an analysis of this paper. It says:

The human hand has 27 degrees of freedom: 4 in each finger, 3 for extension and flexion and one for abduction and adduction; the thumb is more complicated and has 5 DOF, leaving 6 DOF for the rotation and translation of the wrist.

Robots today can perform space missions, solve a Rubik’s cube, sort hospital medication and even make pancakes. But most can’t manage the simple act of grasping a pencil and spinning it around to get a solid grip. Almost all commercially available robots today use mechanical manipulators to perform tasks such as welding, gripping, spinning and so on. KUKA Robotics is a German manufacturer of industrial robots and solutions for factory automation. Their robots are very well designed and capable of interesting things.

But why this post? A few days ago, a University of Washington team of computer scientists and engineers built a robot hand that can not only perform dexterous manipulation but can also learn from its own experience without needing humans to direct it. They spent $300,000 to build a dexterous, 24-DOF, tendon driven, skeleton hand, actuated with a custom made pneumatics system! 40 tendons, 24 joints, and over 130 sensors!

UW CS PhD student Vikash Kumar custom built this robot

The algorithms they developed initially worked well in simulation, but did not map well onto the actual hardware, due to real world reasons like delays in pneumatic control techniques. They then introduced sensors and motion capture systems and a form of machine learning called reinforced learning, which enables the robot to automatically develop better algorithms.

Even though it is too expensive for routine commercial or industrial use, it allows the researchers to push core technologies and test innovative control strategies.

Contrast the above method with the one developed by researchers at Cornell University, iRobot, and University of Chicago about 6 years ago, when they developed a fingerlessuniversal robotic gripper using little more than a balloon and coffee!

An illustration from the paper

The operating principle of this gripper is the ability of granular material to transition between an unjammed deformable state to a jammed state with solid-like rigidity. This approach completely removes the hardware and software complexity involved in the previous one, and also removes computational overheads in calculating precise measurements of locations of fingers in 3D space.

The coffee-filled balloon presses down and deforms around the desired object, and then a vacuum sucks the air out of the balloon, solidifying its grip. When the vacuum is released, the balloon becomes soft again, and the gripper lets go. What sets the jamming-based gripper apart is its good performance with almost any object, including a raw egg or a coin – both notoriously difficult for traditional robotic grippers.

Another set of pictures from the paper

This video from Cornell demonstrates all the features, an exciting watch!

I’ve developed one of these myself, and was able to replicate the results successfully. A word of advice – it does not work well with rice, but did work very well with a specific brand of coffee. If you want to build your own, connecting it directly to an electronically controlled vacuum pump is one option. Another creative idea from the community is to use a simple syringe (needs to be larger than normal, about 40-50ml) and control that using a servo and microcontroller.